






"""simple docstring"""






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCAmelCase_  :					str       =					{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}



def    _A     (__a				)					->			Any:
    """simple docstring"""






    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts				)
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config				)
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights				)

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0



def    _A     (__a       ,							__a				)					->			Union[str, Any]:
    """simple docstring"""






    if args.student_type == "roberta":
        SCREAMING_SNAKE_CASE_ :    List[Any]       		=						False
    elif args.student_type == "gpt2":
        SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						False



def    _A     (__a       ,							__a				)					->			Optional[Any]:
    """simple docstring"""






    if args.student_type == "roberta":
        SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						False



def    _A     ()					->			Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_ :    Dict       		=						argparse.ArgumentParser(description='''Training'''				)
    parser.add_argument('''--force'''       ,							action='''store_true'''       ,							help='''Overwrite dump_path if it already exists.'''				)

    parser.add_argument(
        '''--dump_path'''       ,							type=_lowerCamelCase       ,							required=_lowerCamelCase       ,							help='''The output directory (log, checkpoints, parameters, etc.)'''				)
    parser.add_argument(
        '''--data_file'''       ,							type=_lowerCamelCase       ,							required=_lowerCamelCase       ,							help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''       ,							)

    parser.add_argument(
        '''--student_type'''       ,							type=_lowerCamelCase       ,							choices=['''distilbert''', '''roberta''', '''gpt2''']       ,							required=_lowerCamelCase       ,							help='''The student type (DistilBERT, RoBERTa).'''       ,							)
    parser.add_argument('''--student_config'''       ,							type=_lowerCamelCase       ,							required=_lowerCamelCase       ,							help='''Path to the student configuration.'''				)
    parser.add_argument(
        '''--student_pretrained_weights'''       ,							default=_lowerCamelCase       ,							type=_lowerCamelCase       ,							help='''Load student initialization checkpoint.'''				)

    parser.add_argument(
        '''--teacher_type'''       ,							choices=['''bert''', '''roberta''', '''gpt2''']       ,							required=_lowerCamelCase       ,							help='''Teacher type (BERT, RoBERTa).'''				)
    parser.add_argument('''--teacher_name'''       ,							type=_lowerCamelCase       ,							required=_lowerCamelCase       ,							help='''The teacher model.'''				)

    parser.add_argument('''--temperature'''       ,							default=2.0       ,							type=_lowerCamelCase       ,							help='''Temperature for the softmax temperature.'''				)
    parser.add_argument(
        '''--alpha_ce'''       ,							default=0.5       ,							type=_lowerCamelCase       ,							help='''Linear weight for the distillation loss. Must be >=0.'''				)
    parser.add_argument(
        '''--alpha_mlm'''       ,							default=0.0       ,							type=_lowerCamelCase       ,							help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''       ,							)
    parser.add_argument('''--alpha_clm'''       ,							default=0.5       ,							type=_lowerCamelCase       ,							help='''Linear weight for the CLM loss. Must be >=0.'''				)
    parser.add_argument('''--alpha_mse'''       ,							default=0.0       ,							type=_lowerCamelCase       ,							help='''Linear weight of the MSE loss. Must be >=0.'''				)
    parser.add_argument(
        '''--alpha_cos'''       ,							default=0.0       ,							type=_lowerCamelCase       ,							help='''Linear weight of the cosine embedding loss. Must be >=0.'''				)

    parser.add_argument(
        '''--mlm'''       ,							action='''store_true'''       ,							help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''				)
    parser.add_argument(
        '''--mlm_mask_prop'''       ,							default=0.15       ,							type=_lowerCamelCase       ,							help='''Proportion of tokens for which we need to make a prediction.'''       ,							)
    parser.add_argument('''--word_mask'''       ,							default=0.8       ,							type=_lowerCamelCase       ,							help='''Proportion of tokens to mask out.'''				)
    parser.add_argument('''--word_keep'''       ,							default=0.1       ,							type=_lowerCamelCase       ,							help='''Proportion of tokens to keep.'''				)
    parser.add_argument('''--word_rand'''       ,							default=0.1       ,							type=_lowerCamelCase       ,							help='''Proportion of tokens to randomly replace.'''				)
    parser.add_argument(
        '''--mlm_smoothing'''       ,							default=0.7       ,							type=_lowerCamelCase       ,							help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''       ,							)
    parser.add_argument('''--token_counts'''       ,							type=_lowerCamelCase       ,							help='''The token counts in the data_file for MLM.'''				)

    parser.add_argument(
        '''--restrict_ce_to_mask'''       ,							action='''store_true'''       ,							help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''       ,							)
    parser.add_argument(
        '''--freeze_pos_embs'''       ,							action='''store_true'''       ,							help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''       ,							)
    parser.add_argument(
        '''--freeze_token_type_embds'''       ,							action='''store_true'''       ,							help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''       ,							)

    parser.add_argument('''--n_epoch'''       ,							type=_lowerCamelCase       ,							default=3       ,							help='''Number of pass on the whole dataset.'''				)
    parser.add_argument('''--batch_size'''       ,							type=_lowerCamelCase       ,							default=5       ,							help='''Batch size (for each process).'''				)
    parser.add_argument(
        '''--group_by_size'''       ,							action='''store_false'''       ,							help='''If true, group sequences that have similar length into the same batch. Default is true.'''       ,							)

    parser.add_argument(
        '''--gradient_accumulation_steps'''       ,							type=_lowerCamelCase       ,							default=50       ,							help='''Gradient accumulation for larger training batches.'''       ,							)
    parser.add_argument('''--warmup_prop'''       ,							default=0.05       ,							type=_lowerCamelCase       ,							help='''Linear warmup proportion.'''				)
    parser.add_argument('''--weight_decay'''       ,							default=0.0       ,							type=_lowerCamelCase       ,							help='''Weight decay if we apply some.'''				)
    parser.add_argument('''--learning_rate'''       ,							default=5e-4       ,							type=_lowerCamelCase       ,							help='''The initial learning rate for Adam.'''				)
    parser.add_argument('''--adam_epsilon'''       ,							default=1e-6       ,							type=_lowerCamelCase       ,							help='''Epsilon for Adam optimizer.'''				)
    parser.add_argument('''--max_grad_norm'''       ,							default=5.0       ,							type=_lowerCamelCase       ,							help='''Max gradient norm.'''				)
    parser.add_argument('''--initializer_range'''       ,							default=0.02       ,							type=_lowerCamelCase       ,							help='''Random initialization range.'''				)

    parser.add_argument(
        '''--fp16'''       ,							action='''store_true'''       ,							help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''       ,							)
    parser.add_argument(
        '''--fp16_opt_level'''       ,							type=_lowerCamelCase       ,							default='''O1'''       ,							help=(
            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
            '''See details at https://nvidia.github.io/apex/amp.html'''
        )       ,							)
    parser.add_argument('''--n_gpu'''       ,							type=_lowerCamelCase       ,							default=1       ,							help='''Number of GPUs in the node.'''				)
    parser.add_argument('''--local_rank'''       ,							type=_lowerCamelCase       ,							default=-1       ,							help='''Distributed training - Local rank'''				)
    parser.add_argument('''--seed'''       ,							type=_lowerCamelCase       ,							default=56       ,							help='''Random seed'''				)

    parser.add_argument('''--log_interval'''       ,							type=_lowerCamelCase       ,							default=5_00       ,							help='''Tensorboard logging interval.'''				)
    parser.add_argument('''--checkpoint_interval'''       ,							type=_lowerCamelCase       ,							default=40_00       ,							help='''Checkpoint interval.'''				)
    SCREAMING_SNAKE_CASE_ :    Tuple       		=						parser.parse_args()
    sanity_checks(_lowerCamelCase				)

    # ARGS #
    init_gpu_params(_lowerCamelCase				)
    set_seed(_lowerCamelCase				)
    if args.is_master:
        if os.path.exists(args.dump_path				):
            if not args.force:
                raise ValueError(
                    f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                    ''' itUse `--force` if you want to overwrite it'''				)
            else:
                shutil.rmtree(args.dump_path				)

        if not os.path.exists(args.dump_path				):
            os.makedirs(args.dump_path				)
        logger.info(f'Experiment will be dumped and logged in {args.dump_path}'				)

        # SAVE PARAMS #
        logger.info(f'Param: {args}'				)
        with open(os.path.join(args.dump_path       ,							'''parameters.json'''				)       ,							'''w'''				) as f:
            json.dump(vars(_lowerCamelCase				)       ,							_lowerCamelCase       ,							indent=4				)
        git_log(args.dump_path				)

    SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_ :    int       		=						MODEL_CLASSES[args.student_type]
    SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_ :    str       		=						MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    SCREAMING_SNAKE_CASE_ :    Dict       		=						teacher_tokenizer_class.from_pretrained(args.teacher_name				)
    SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						{}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        SCREAMING_SNAKE_CASE_ :    List[Any]       		=						tokenizer.all_special_tokens.index(_lowerCamelCase				)
        SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						tokenizer.all_special_ids[idx]
    logger.info(f'Special tokens {special_tok_ids}'				)
    SCREAMING_SNAKE_CASE_ :    int       		=						special_tok_ids
    SCREAMING_SNAKE_CASE_ :    str       		=						tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(f'Loading data from {args.data_file}'				)
    with open(args.data_file       ,							'''rb'''				) as fp:
        SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						pickle.load(_lowerCamelCase				)

    if args.mlm:
        logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'				)
        with open(args.token_counts       ,							'''rb'''				) as fp:
            SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						pickle.load(_lowerCamelCase				)

        SCREAMING_SNAKE_CASE_ :    int       		=						np.maximum(_lowerCamelCase       ,							1				) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            SCREAMING_SNAKE_CASE_ :    Dict       		=						0.0  # do not predict special tokens
        SCREAMING_SNAKE_CASE_ :    Dict       		=						torch.from_numpy(_lowerCamelCase				)
    else:
        SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						None

    SCREAMING_SNAKE_CASE_ :    Dict       		=						LmSeqsDataset(params=_lowerCamelCase       ,							data=_lowerCamelCase				)
    logger.info('''Data loader created.'''				)

    # STUDENT #
    logger.info(f'Loading student config from {args.student_config}'				)
    SCREAMING_SNAKE_CASE_ :    int       		=						student_config_class.from_pretrained(args.student_config				)
    SCREAMING_SNAKE_CASE_ :    List[Any]       		=						True

    if args.student_pretrained_weights is not None:
        logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'				)
        SCREAMING_SNAKE_CASE_ :    str       		=						student_model_class.from_pretrained(args.student_pretrained_weights       ,							config=_lowerCamelCase				)
    else:
        SCREAMING_SNAKE_CASE_ :    List[Any]       		=						student_model_class(_lowerCamelCase				)

    if args.n_gpu > 0:
        student.to(f'cuda:{args.local_rank}'				)
    logger.info('''Student loaded.'''				)

    # TEACHER #
    SCREAMING_SNAKE_CASE_ :    List[Any]       		=						teacher_model_class.from_pretrained(args.teacher_name       ,							output_hidden_states=_lowerCamelCase				)
    if args.n_gpu > 0:
        teacher.to(f'cuda:{args.local_rank}'				)
    logger.info(f'Teacher loaded from {args.teacher_name}.'				)

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(_lowerCamelCase       ,							_lowerCamelCase				)
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(_lowerCamelCase       ,							_lowerCamelCase				)

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0				) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						Distiller(
        params=_lowerCamelCase       ,							dataset=_lowerCamelCase       ,							token_probs=_lowerCamelCase       ,							student=_lowerCamelCase       ,							teacher=_lowerCamelCase				)
    distiller.train()
    logger.info('''Let\'s go get some drinks.'''				)


if __name__ == "__main__":
  main()




"""simple docstring"""






class 			lowercase					:






			def __init__(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {}






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	None:





				"""simple docstring"""


				print(self.vertex			)
				for i in self.vertex:
					print(SCREAMING_SNAKE_CASE_      ,''' -> '''      ,''' -> '''.join([str(SCREAMING_SNAKE_CASE_			) for j in self.vertex[i]]			)			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	None:





				"""simple docstring"""


				if from_vertex in self.vertex:
					self.vertex[from_vertex].append(SCREAMING_SNAKE_CASE_			)
				else:
					# else make a new vertex
					lowerCAmelCase												=  [to_vertex]






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [False] * len(self.vertex			)

				# call the recursive helper function
				for i in range(len(self.vertex			)			):
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	list			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  True

				print(SCREAMING_SNAKE_CASE_      ,end=''' '''			)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
					UpperCAmelCase        =				Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("""DFS:""")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3


